zoeygxy commented on a change in pull request #15942: Refines NDArray indexing
and adds numpy ndarray indexing [READY FOR REVIEW]
URL: https://github.com/apache/incubator-mxnet/pull/15942#discussion_r317962266
##########
File path: tests/python/unittest/test_numpy_ndarray.py
##########
@@ -421,208 +468,217 @@ def assert_same(np_array, np_index, mx_array,
mx_index, mx_value, np_value=None)
np_index.append(idx)
np_index = tuple(np_index)
- mx_array = np.array(np_array, dtype=np_array.dtype)
- np_array = mx_array.asnumpy()
- indexed_array_shape = np_array[np_index].shape
- np_indexed_array = _np.random.randint(low=-10000, high=0,
size=indexed_array_shape)
- # test value is a numpy array without broadcast
- assert_same(np_array, np_index, mx_array, index, np_indexed_array)
- # test value is an numeric_type
- assert_same(np_array, np_index, mx_array, index,
_np.random.randint(low=-10000, high=0))
- if len(indexed_array_shape) > 1:
- # test ndarray with broadcast
- assert_same(np_array, np_index, mx_array, index,
- _np.random.uniform(low=-10000, high=0,
size=(indexed_array_shape[-1],)))
- # test numpy array with broadcast
- assert_same(np_array, np_index, mx_array, index,
- _np.random.randint(low=-10000, high=0,
size=(indexed_array_shape[-1],)))
- # test list with broadcast
- assert_same(np_array, np_index, mx_array, index,
- [_np.random.randint(low=-10000, high=0)] *
indexed_array_shape[-1])
+ mx_array = np.array(np_array, dtype=np_array.dtype) # mxnet.np.ndarray
+ np_array = mx_array.asnumpy() # native numpy array
+ if is_scalar:
+ # test value is a numeric type
+ assert_same(np_array, np_index, mx_array, index,
_np.random.randint(low=-10000, high=0))
+ value_nd = [_np.random.randint(low=-10000, high=0)]
+ assert_same(np_array, np_index, mx_array, index, value_nd,
value_nd[0])
+ else:
+ indexed_array_shape = np_array[np_index].shape
+ np_indexed_array = _np.random.randint(low=-10000, high=0,
size=indexed_array_shape)
+ # test value is a native numpy array without broadcast
+ assert_same(np_array, np_index, mx_array, index, np_indexed_array)
+ # test value is a mxnet numpy array without broadcast
+ assert_same(np_array, np_index, mx_array, index,
np.array(np_indexed_array))
+ # test value is an numeric_type
+ assert_same(np_array, np_index, mx_array, index,
_np.random.randint(low=-10000, high=0))
+ if len(indexed_array_shape) > 1:
+ np_value = _np.random.randint(low=-10000, high=0,
size=(indexed_array_shape[-1],))
+ # test mxnet ndarray with broadcast
+ assert_same(np_array, np_index, mx_array, index,
np.array(np_value))
+ # test native numpy array with broadcast
+ assert_same(np_array, np_index, mx_array, index, np_value)
+ # test list with broadcast
+ assert_same(np_array, np_index, mx_array, index,
+ [_np.random.randint(low=-10000, high=0)] *
indexed_array_shape[-1])
def test_getitem_autograd(np_array, index):
+ """
+ np_array: native numpy array.
+ """
x = np.array(np_array, dtype=np_array.dtype)
x.attach_grad()
- with autograd.record():
+ with mx.autograd.record():
y = x[index]
+ if not isinstance(y, np.ndarray):
+ return
y.backward()
value = np.ones_like(y)
x_grad = np.zeros_like(x)
x_grad[index] = value
assert same(x_grad.asnumpy(), x.grad.asnumpy())
def test_setitem_autograd(np_array, index):
+ """
+ np_array: native numpy array.
+ """
x = np.array(np_array, dtype=np_array.dtype)
+ if not isinstance(x[index], np.ndarray):
+ return # x[index] is scalar
out_shape = x[index].shape
y = np.array(_np.random.uniform(size=out_shape))
y.attach_grad()
try:
- with autograd.record():
+ with mx.autograd.record():
x[index] = y
- assert False # should not reach here
+ x.backward()
+ y_grad = np.ones_like(y)
+ assert same(y_grad.asnumpy(), y.grad.asnumpy())
except mx.base.MXNetError as err:
assert str(err).find('Inplace operations (+=, -=, x[:]=, etc) are
not supported when recording with') != -1
- def np_int(index, int_type=_np.int32):
- def convert(num):
- if num is None:
- return num
- else:
- return int_type(num)
-
- if isinstance(index, slice):
- return slice(convert(index.start), convert(index.stop),
convert(index.step))
- elif isinstance(index, tuple): # tuple of slices and integers
- ret = []
- for elem in index:
- if isinstance(elem, slice):
- ret.append(slice(convert(elem.start), convert(elem.stop),
convert(elem.step)))
- else:
- ret.append(convert(elem))
- return tuple(ret)
- else:
- assert False
-
shape = (8, 16, 9, 9)
- np_array = _np.arange(_np.prod(shape), dtype='int32').reshape(shape)
+ np_array = _np.arange(_np.prod(_np.array(shape)),
dtype='int32').reshape(shape) # native np array
index_list = [
- (),
- 0,
- _np.int32(0),
- _np.int64(0),
- 5,
- _np.int32(5),
- _np.int64(5),
- -1,
- _np.int32(-1),
- _np.int64(-1),
- slice(5),
- np_int(slice(5), _np.int32),
- np_int(slice(5), _np.int64),
- slice(1, 5),
- np_int(slice(1, 5), _np.int32),
- np_int(slice(1, 5), _np.int64),
- slice(1, 5, 2),
- np_int(slice(1, 5, 2), _np.int32),
- np_int(slice(1, 5, 2), _np.int64),
- slice(7, 0, -1),
- np_int(slice(7, 0, -1)),
- np_int(slice(7, 0, -1), _np.int64),
- slice(None, 6),
- np_int(slice(None, 6)),
- np_int(slice(None, 6), _np.int64),
- slice(None, 6, 3),
- np_int(slice(None, 6, 3)),
- np_int(slice(None, 6, 3), _np.int64),
- slice(1, None),
- np_int(slice(1, None)),
- np_int(slice(1, None), _np.int64),
- slice(1, None, 3),
- np_int(slice(1, None, 3)),
- np_int(slice(1, None, 3), _np.int64),
- slice(None, None, 2),
- np_int(slice(None, None, 2)),
- np_int(slice(None, None, 2), _np.int64),
- slice(None, None, -1),
- np_int(slice(None, None, -1)),
- np_int(slice(None, None, -1), _np.int64),
- slice(None, None, -2),
- np_int(slice(None, None, -2), _np.int32),
- np_int(slice(None, None, -2), _np.int64),
- (slice(None), slice(None), 1, 8),
- (slice(None), slice(None), -1, 8),
- (slice(None), slice(None), 1, -8),
- (slice(None), slice(None), -1, -8),
- np_int((slice(None), slice(None), 1, 8)),
- np_int((slice(None), slice(None), 1, 8), _np.int64),
- (slice(None), slice(None), 1, 8),
- np_int((slice(None), slice(None), -1, -8)),
- np_int((slice(None), slice(None), -1, -8), _np.int64),
- (slice(None), 2, slice(1, 5), 1),
- np_int((slice(None), 2, slice(1, 5), 1)),
- np_int((slice(None), 2, slice(1, 5), 1), _np.int64),
- (1, 2, 3),
- np_int((1, 2, 3)),
- np_int((1, 2, 3), _np.int64),
- (-1, -2, -3),
- np_int((-1, -2, -3)),
- np_int((-1, -2, -3), _np.int64),
- (1, 2, 3, 4),
- np_int((1, 2, 3, 4)),
- np_int((1, 2, 3, 4), _np.int64),
- (-4, -3, -2, -1),
- np_int((-4, -3, -2, -1)),
- np_int((-4, -3, -2, -1), _np.int64),
- (slice(None, None, -1), 2, slice(1, 5), 1),
- np_int((slice(None, None, -1), 2, slice(1, 5), 1)),
- np_int((slice(None, None, -1), 2, slice(1, 5), 1), _np.int64),
- (slice(None, None, -1), 2, slice(1, 7, 2), 1),
- np_int((slice(None, None, -1), 2, slice(1, 7, 2), 1)),
- np_int((slice(None, None, -1), 2, slice(1, 7, 2), 1), _np.int64),
- (slice(1, 8, 2), slice(14, 2, -2), slice(3, 8), slice(0, 7, 3)),
- np_int((slice(1, 8, 2), slice(14, 2, -2), slice(3, 8), slice(0, 7,
3))),
- np_int((slice(1, 8, 2), slice(14, 2, -2), slice(3, 8), slice(0, 7,
3)), _np.int64),
- (slice(1, 8, 2), 1, slice(3, 8), 2),
- np_int((slice(1, 8, 2), 1, slice(3, 8), 2)),
- np_int((slice(1, 8, 2), 1, slice(3, 8), 2), _np.int64),
- [1],
- [1, 2],
- [2, 1, 3],
- [7, 5, 0, 3, 6, 2, 1],
- _np.array([6, 3], dtype=_np.int32),
- _np.array([[3, 4], [0, 6]], dtype=_np.int32),
- _np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=_np.int32),
- _np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=_np.int64),
- _np.array([[2], [0], [1]], dtype=_np.int32),
- _np.array([[2], [0], [1]], dtype=_np.int64),
- np.array([4, 7], dtype=_np.int32),
- np.array([4, 7], dtype=_np.int64),
- np.array([[3, 6], [2, 1]], dtype=_np.int32),
- np.array([[3, 6], [2, 1]], dtype=_np.int64),
- np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=_np.int32),
- np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=_np.int64),
- (1, [2, 3]),
- (1, [2, 3], _np.array([[3], [0]], dtype=_np.int32)),
- (1, [2, 3]),
- (1, [2, 3], _np.array([[3], [0]], dtype=_np.int64)),
- (1, [2], _np.array([[5], [3]], dtype=_np.int32), slice(None)),
- (1, [2], _np.array([[5], [3]], dtype=_np.int64), slice(None)),
- (1, [2, 3], _np.array([[6], [0]], dtype=_np.int32), slice(2, 5)),
- (1, [2, 3], _np.array([[6], [0]], dtype=_np.int64), slice(2, 5)),
- (1, [2, 3], _np.array([[4], [7]], dtype=_np.int32), slice(2, 5, 2)),
- (1, [2, 3], _np.array([[4], [7]], dtype=_np.int64), slice(2, 5, 2)),
- (1, [2], _np.array([[3]], dtype=_np.int32), slice(None, None, -1)),
- (1, [2], _np.array([[3]], dtype=_np.int64), slice(None, None, -1)),
- (1, [2], _np.array([[3]], dtype=_np.int32), np.array([[5, 7], [2, 4]],
dtype=_np.int64)),
- (1, [2], np.array([[4]], dtype=_np.int32), np.array([[1, 3], [5, 7]],
dtype='int64')),
- [0],
- [0, 1],
- [1, 2, 3],
- [2, 0, 5, 6],
- ([1, 1], [2, 3]),
- ([1], [4], [5]),
- ([1], [4], [5], [6]),
- ([[1]], [[2]]),
- ([[1]], [[2]], [[3]], [[4]]),
- (slice(0, 2), [[1], [6]], slice(0, 2), slice(0, 5, 2)),
- ([[[[1]]]], [[1]], slice(0, 3), [1, 5]),
- ([[[[1]]]], 3, slice(0, 3), [1, 3]),
- ([[[[1]]]], 3, slice(0, 3), 0),
- ([[[[1]]]], [[2], [12]], slice(0, 3), slice(None)),
- ([1, 2], slice(3, 5), [2, 3], [3, 4]),
- ([1, 2], slice(3, 5), (2, 3), [3, 4]),
- range(4),
- range(3, 0, -1),
- (range(4,), [1]),
- # slice(0, 0) does not support output zero-size tensor yet
+ # Basic indexing
+ # Single int as index
+ (0, False), (np.int32(0), False), (np.int64(0), False),
Review comment:
Fixed. Thx!
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